Welcome to our website, where we specialize in providing students with engaging and hands-on image enhancement projects. Our projects are designed to help students develop their technical skills, as well as their creativity, in the field of image processing.
Project Code: TMMAIP476
Project Title:VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNetB0 with XGBoost-Enhancing Bone Fracture Classification in X-Ray Using Deep Learning ModelsView DetailsProject Code: TMMAIP471
Project Title:Enhancing Hybrid Classification for Plant Diseases with Deep Feature Selection Based on Analytical Entropy and Statistical MethodView DetailsProject Code: TMMAIP469
Project Title:Masked Vascular Structure Segmentation and Completion in Retinal ImagesView DetailsProject Code: TMMAIP468
Project Title:Low contrast enhancement algorithm for color image using pythagorean fuzzy sets with a fusion of CLAHE and BPDHE methodsView DetailsProject Code: TMMAIP467
Project Title:Recovering the Images Distorted by Surface Waves Using an Efficient Multi-Stage Image Reconstruction StrategyView DetailsProject Code: TMMAIP462
Project Title:A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image EnhancementView DetailsProject Code: TMMAIP463
Project Title:Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image EnhancementView DetailsProject Code: TMMAIP464
Project Title:Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image AnalysisView DetailsProject Code: TMMAIP465
Project Title:Heart Disease Classification from Echocardiogram Images Using Deep LearningView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TMMAIP476 | VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNet... | |
| 2 | TMMAIP474 | Bone Age Estimation from Hand X-ray Images | |
| 3 | TMMAIP471 | Enhancing Hybrid Classification for Plant Diseases with Deep Feature S... | |
| 4 | TMMAIP469 | Masked Vascular Structure Segmentation and Completion in Retinal Image... | |
| 5 | TMMAIP468 | Low contrast enhancement algorithm for color image using pythagorean f... | |
| 6 | TMMAIP467 | Recovering the Images Distorted by Surface Waves Using an Efficient M... | |
| 7 | TMMAIP462 | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserv... | |
| 8 | TMMAIP463 | Contrast Limited Adaptive Local Histogram Equalization Method for Poor... | |
| 9 | TMMAIP464 | Performance Evaluation of Support Vector Machine and Stacked Autoencod... | |
| 10 | TMMAIP465 | Heart Disease Classification from Echocardiogram Images Using Deep Lea... |
Project Code: TMMAIP476
Project Title:VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNetB0 with XGBoost-Enhancing Bone Fracture Classification in X-Ray Using Deep Learning ModelsView DetailsProject Code: TMMAIP471
Project Title:Enhancing Hybrid Classification for Plant Diseases with Deep Feature Selection Based on Analytical Entropy and Statistical MethodView DetailsProject Code: TMMAIP469
Project Title:Masked Vascular Structure Segmentation and Completion in Retinal ImagesView DetailsProject Code: TMMAIP468
Project Title:Low contrast enhancement algorithm for color image using pythagorean fuzzy sets with a fusion of CLAHE and BPDHE methodsView DetailsProject Code: TMMAIP467
Project Title:Recovering the Images Distorted by Surface Waves Using an Efficient Multi-Stage Image Reconstruction StrategyView DetailsProject Code: TMMAIP462
Project Title:A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image EnhancementView DetailsProject Code: TMMAIP463
Project Title:Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image EnhancementView DetailsProject Code: TMMAIP464
Project Title:Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image AnalysisView DetailsProject Code: TMMAIP465
Project Title:Heart Disease Classification from Echocardiogram Images Using Deep LearningView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TMMAIP476 | VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNet... | |
| 2 | TMMAIP474 | Bone Age Estimation from Hand X-ray Images | |
| 3 | TMMAIP471 | Enhancing Hybrid Classification for Plant Diseases with Deep Feature S... | |
| 4 | TMMAIP469 | Masked Vascular Structure Segmentation and Completion in Retinal Image... | |
| 5 | TMMAIP468 | Low contrast enhancement algorithm for color image using pythagorean f... | |
| 6 | TMMAIP467 | Recovering the Images Distorted by Surface Waves Using an Efficient M... | |
| 7 | TMMAIP462 | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserv... | |
| 8 | TMMAIP463 | Contrast Limited Adaptive Local Histogram Equalization Method for Poor... | |
| 9 | TMMAIP464 | Performance Evaluation of Support Vector Machine and Stacked Autoencod... | |
| 10 | TMMAIP465 | Heart Disease Classification from Echocardiogram Images Using Deep Lea... |
Project Code: TMMAIP476
Project Title:VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNetB0 with XGBoost-Enhancing Bone Fracture Classification in X-Ray Using Deep Learning ModelsView DetailsProject Code: TMMAIP471
Project Title:Enhancing Hybrid Classification for Plant Diseases with Deep Feature Selection Based on Analytical Entropy and Statistical MethodView DetailsProject Code: TMMAIP469
Project Title:Masked Vascular Structure Segmentation and Completion in Retinal ImagesView DetailsProject Code: TMMAIP468
Project Title:Low contrast enhancement algorithm for color image using pythagorean fuzzy sets with a fusion of CLAHE and BPDHE methodsView DetailsProject Code: TMMAIP467
Project Title:Recovering the Images Distorted by Surface Waves Using an Efficient Multi-Stage Image Reconstruction StrategyView DetailsProject Code: TMMAIP462
Project Title:A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image EnhancementView DetailsProject Code: TMMAIP463
Project Title:Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image EnhancementView DetailsProject Code: TMMAIP464
Project Title:Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image AnalysisView DetailsProject Code: TMMAIP465
Project Title:Heart Disease Classification from Echocardiogram Images Using Deep LearningView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TMMAIP476 | VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNet... | |
| 2 | TMMAIP474 | Bone Age Estimation from Hand X-ray Images | |
| 3 | TMMAIP471 | Enhancing Hybrid Classification for Plant Diseases with Deep Feature S... | |
| 4 | TMMAIP469 | Masked Vascular Structure Segmentation and Completion in Retinal Image... | |
| 5 | TMMAIP468 | Low contrast enhancement algorithm for color image using pythagorean f... | |
| 6 | TMMAIP467 | Recovering the Images Distorted by Surface Waves Using an Efficient M... | |
| 7 | TMMAIP462 | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserv... | |
| 8 | TMMAIP463 | Contrast Limited Adaptive Local Histogram Equalization Method for Poor... | |
| 9 | TMMAIP464 | Performance Evaluation of Support Vector Machine and Stacked Autoencod... | |
| 10 | TMMAIP465 | Heart Disease Classification from Echocardiogram Images Using Deep Lea... |
We believe that image enhancement is an important aspect of digital media and should be included in the curriculum of any student pursuing a career in this field. Our image enhancement projects are designed to challenge students to think critically, experiment with different techniques, and ultimately produce stunning images.
Takeoff Edu Group projects are designed for all levels, from beginner to advanced, and cover a wide range of image enhancement projects topics, including color correction, brightness and contrast adjustment, and noise reduction. Our team of experienced professionals will guide you through each step of the project, providing support and feedback along the way.
At the end of each project, you will have a portfolio-ready image that they can proudly display to future employers. Our image enhancement projects also provide students with a competitive edge, as they learn to use the latest tools and techniques in the field of image enhancement.
Are You Looking for the Image Enhancement Projects & Support?
If you're looking for a fun and engaging way to improve your image enhancement skills, look no further Takeoff Edu Group! Our projects are designed to help you achieve your goals, no matter what your level of experience is. Enroll today and start enhancing your images like a pro!
Ready to get started? Contact us today to learn more about our image enhancement projects and how we can help you achieve your image enhancement goals.
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
