A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing

TL;DR

This paper introduces a convex model for edge-histogram specification that directly uses image gradients, enabling efficient edge-preserving smoothing with various applications.

Contribution

The paper proposes a convex gradient-based model for edge-histogram specification, improving computational efficiency and constraint handling over previous non-convex approaches.

Findings

Efficient computation using ADMM and FISTA algorithms.

Successful application to image abstraction, edge extraction, and other tasks.

Produces high-quality results with reduced computational cost.

Abstract

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.