The guided filter and its subsequent derivatives have been widely employed in many image processing and computer vision applications primarily brought about by their low complexity and good edge-preservation properties. Despite this success, the different variants of the guided filter are unable to handle more aggressive filtering strengths leading to the manifestation of "detail halos". At the same time, these existing filters perform poorly when the input and guide images have structural inconsistencies. In this paper, we demonstrate that these limitations are due to the guided filter operating as a variable-strength locally-isotropic filter that, in effect, acts as a weak anisotropic filter on the image. Our analysis shows that this behaviour stems from the use of unweighted averaging in the final steps of guided filter variants including the adaptive guided filter (AGF), weighted guided image filter (WGIF), and gradient-domain guided image filter (GGIF). We propose a novel filter, the Anisotropic Guided Filter (AnisGF), that utilises weighted averaging to achieve maximum diffusion while preserving strong edges in the image. The proposed weights are optimised based on the local neighbourhood variances to achieve strong anisotropic filtering while preserving the low computational cost of the original guided filter. Synthetic tests show that the proposed method addresses the presence of detail halos and the handling of inconsistent structures found in previous variants of the guided filter. Furthermore, experiments in scale-aware filtering, detail enhancement, texture removal, and chroma upsampling demonstrate the improvements brought about by the technique.

Edge-preserving filtering is vital in many image processing and computer vision tasks. However, existing techniques, such as the bilateral filter and the guided filter, have limitations when dealing with stronger filtering strengths, resulting in the suppression of image structures. In this work, we discuss an alternative structure-preserving image filter (SPIF) that operates on multiple detail densities (i.e. scales) so as to overcome the trade-off between filtering strength and edge-preservation. We show, using several experiments, that the proposed filter is capable of performing various tasks, specifically detail enhancement and image abstraction, while maintaining low computational times. The parameterization of the proposed filter provides a high degree of flexibility, allowing it to perform well for the aforementioned tasks, and potentially, in many other tasks.

The anaglyph is a widely overlooked method of viewing three-dimensional images on any colored display. This is done by selectively filtering the image through colored lenses. Despite the simplicity of this system, the approach to designing anaglyph images remained largely empirical until a recent mathematical analysis by Eric Dubois. While the methods shown in the said work create good anaglyphs, they still exhibit a large amount of retinal rivalry which makes anaglyphs uncomfortable to view. This paper tackles modifications to the said approach to tackle several anaglyph issues, namely ghosting, retinal rivalry, and color reproduction, simultaneously. Subjective testing showed an improvement in viewer acceptance of images designed using the proposed method.

Stock market analysis has traditionally been proven to be difficult due to the large amount of noise present in the data. Different approaches have been proposed to predict stock prices including the use of computational intelligence and data mining techniques. Many of these methods operate on closing stock prices or on known technical indicators. Limited studies have shown that Japanese candlestick analysis serve as rich information sources for the market. In this paper decision trees based on the ID3 algorithm are used to derive short-term trading decisions from candlesticks. To handle the large amount of uncertainty in the data, both inputs and output classifications are fuzzified using well-defined membership functions. Testing results of the derived decision trees show significant gains compared to ideal mid and long-term trading simulations both in frictionless and realistic markets.