Learning Converged Propagations With Deep Prior Ensemble for Image Enhancement


Various vision and learning applications rely heavily on improving the visual quality of images. Recently, researchers have looked into both knowledge-driven maximum a posterior (MAP) with prior models and totally data-dependent convolutional neural network (CNN) techniques to tackle specific enhancement challenges. A unified framework, called deep prior ensemble (DPE), is proposed in this research by utilising the advantages of these two types of processes within a complimentary propagation approach. To be more specific, we start with a basic propagation method based on fundamental picture modelling cues and then incorporate residual CNNs to help forecast the propagation direction at each level. With prior projections, we can theoretically verify that even with experience-inspired CNNs, DPE is convergent and its output always meets our fundamental task requirements. Because our descent routes are learned from training data, we have an advantage over standard optimization-based MAP techniques in that they are substantially more resistant to local minimums. DPE, on the other hand, is able to take advantage of the rich task cues examined on the basis of domain knowledge as compared to existing CNN type networks. As a result, the DPE offers a generic ensemble methodology for various image improvement tasks that incorporates both knowledge and data-based signals. Furthermore, our theoretical investigations show that the DPE feed-forward propagations are properly controlled towards our desired solution. On a number of image-enhancing tasks, the suggested DPE beats current methods, both quantitatively and visually, according to the findings of experiments.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : An Analytical Approach for Soil and Land Classification System using Image Processing ABSTRACT: Land mapping and classification have piqued the interest of experts in recent decades for a variety of reasons.
PROJECT TITLE : Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM ABSTRACT: This paper proposes a peach disease detection method based on the asymptotic non-local means (ANLM) image algorithm
PROJECT TITLE : 3D Neuron Reconstruction in Tangled Neuronal Image With Deep Networks ABSTRACT: Understanding how the brain works requires tracing or digital reconstruction of 3D neuron models. For the clean neuronal image with
PROJECT TITLE : A Biological Vision Inspired Framework for Image Enhancement in Poor Visibility Conditions ABSTRACT: For many computer vision applications, image augmentation is an essential pre-processing step, especially for
PROJECT TITLE : A Multi-Domain and Multi-Modal Representation Disentangler for Cross-Domain Image Manipulation and Classification ABSTRACT: Deep learning and computer vision have been focusing on the development of interpretable

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry