Non-blind Image Restoration Based on Convolutional Neural Network

Blind image restoration processors based on convolutional neural network (CNN) are intensively researched because of their high performance. However, they are too sensitive to the perturbation of the degradation model. They easily fail to restore the image whose degradation model is slightly different from the trained degradation model. In this paper, we propose a non-blind CNN-based image restoration processor, aiming to be robust against a perturbation of the degradation model compared to the blind restoration processor. Experimental comparisons demonstrate that the proposed non-blind CNN-based image restoration processor can robustly restore images compared to existing blind CNN-based image restoration processors.Comment: Accepted by IEEE 7th Global Conference on Consumer Electronics, 201

ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos

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Semi-Sparsity for Smoothing Filters

In this paper, we propose an interesting semi-sparsity smoothing algorithm based on a novel sparsity-inducing optimization framework. This method is derived from the multiple observations, that is, semi-sparsity prior knowledge is more universally applicable, especially in areas where sparsity is not fully admitted, such as polynomial-smoothing surfaces. We illustrate that this semi-sparsity can be identified into a generalized $L_0$-norm minimization in higher-order gradient domains, thereby giving rise to a new "feature-aware" filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces). Notice that a direct solver is always unavailable due to the non-convexity and combinatorial nature of $L_0$-norm minimization. Instead, we solve the model based on an efficient half-quadratic splitting minimization with fast Fourier transforms (FFTs) for acceleration. We finally demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications

Exploring Methods for Holistically Improving Drawing Ability With Artificial Intelligence

Drawing is a highly useful skill that can make people better at solving problems, communicating ideas to others, collaborating, and producing more creative and novel ideas. It can be a difficult skill to master for many people, however. Like any learned skill, it requires many hours of practice for noticeable improvement, and sufficient motivation is also necessary to keep practicing consistently over a period of time. Utilizing sketch recognition and other forms of artificial intelligence to assist in learning to draw may facilitate the necessary improvements in self-efficacy and motivation students need to improve their drawing ability. While similar tools have been explored, there has been little to no effort at designing a truly holistic approach for teaching drawing skills that includes the basic fundamentals and building blocks for drawing any 3-dimensional object. This dissertation explored the potential of an intelligent tutoring system for teaching drawing skills called SketchTivity along with various other technology probes focused on drawing. We found evidence that individuals could build confidence, build motivation, make measurable improvements to drawing ability, and reduce fixation when ideating concepts through the various studies we conducted. We developed a flexible perspective accuracy recognition algorithm that can help individuals learn perspective. In interviews with students and teachers who used SketchTivity we discovered benefits and limitations of the system. Students were engaged by the interactive lessons, motivated by the gameplay, and saw it as a great warm-up tool. Meanwhile instructors loved that the system could offload grading tasks for them. We hope the nuances of this potential will inform the future development and promise of the approaches described in this dissertation along with similar approaches to impact education at large