The motion or out-of-focus effect in digital images is the main reason for
the blurred regions in defocused-blurred images. It may adversely affect
various image features such as texture, pixel, and region. Therefore, it is
important to detect in-focused objects in defocused-blurred images after the
segmentation of blurred and non-blurred regions. The state-of-the-art
techniques are prone to noisy pixels, and their local descriptors for
developing segmentation metrics are also complex. To address these issues, this
research, therefore, proposed a novel and hybrid-focused detection approach
based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN)
structure. The proposed approach partially resolves the limitations of the
existing contrast schemes to detect in-focused smooth objects from the
out-of-focused smooth regions in the defocus dataset. The visual and
quantitative evaluation illustrates that the proposed approach outperformed in
terms of accuracy and efficiency to referenced algorithms. The highest F-score
of the proposed approach on Zhao's dataset is 0.7940 whereas on Shi's dataset
is 0.9178.

这篇论文介绍了一种基于离散余弦变换（DCT）系数和感知共振神经网络（PCNN）结构的新型混合焦点检测方法，有效地检测出失焦图像中清晰物体，且在准确性和效率方面优于其他算法。