2018/06/21 by Kelvin C. K. Chan, Raymond H. Chan, Chan, Kelvin C. K. +3
Computer Science · Engineering · #Advanced Neural Network Applications #Advancements in Photolithography Techniques #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1806.08101
arxiv created 2018/06/21 · openalex publication_date 2018/06/21 · arxiv updated 2018/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem [M. Mignotte. An energy-based model for the image edge-histogram specification problem. IEEE Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. In this paper, instead of considering edge magnitudes, we directly consider the image gradients and propose a convex model based on them. Furthermore, we include additional constraints in our model based on different applications. The convexity of our model allows us to compute the output image efficiently using either Alternating Direction Method of Multipliers or Fast Iterative Shrinkage-Thresholding Algorithm. We consider several applications in edge-preserving smoothing including image abstraction, edge extraction, details exaggeration, and documents scan-through removal. Numerical results are given to illustrate that our method successfully produces decent results efficiently.