This paper introduces a frequency-tuned salient region detection method that produces full-resolution saliency maps with well-defined boundaries of salient objects. The method retains more frequency content from the original image than existing techniques, leading to better performance in terms of precision and recall. It uses color and luminance features, is simple to implement, and is computationally efficient. The method is compared to five state-of-the-art salient region detection methods using frequency domain analysis, ground truth, and a salient object segmentation application. The proposed method outperforms the five algorithms in both ground-truth evaluation and segmentation tasks. The paper discusses the limitations of existing saliency maps, including low resolution, poorly defined object boundaries, and failure to uniformly map entire salient regions. It then presents a frequency-domain analysis of five state-of-the-art saliency detection methods, showing that they primarily use extremely low-frequency content. The proposed method introduces a frequency-tuned approach to estimate center-surround contrast using color and luminance features, offering three advantages: uniformly highlighted salient regions with well-defined boundaries, full resolution, and computational efficiency. The paper also discusses general approaches to determining saliency, including biological-based, purely computational, and hybrid methods. It highlights the limitations of existing methods, such as low resolution, ill-defined object boundaries, and failure to uniformly map entire salient regions. The paper then presents a frequency-domain analysis of five state-of-the-art saliency detection methods, showing that they primarily use extremely low-frequency content. The proposed method introduces a frequency-tuned approach to estimate center-surround contrast using color and luminance features, offering three advantages: uniformly highlighted salient regions with well-defined boundaries, full resolution, and computational efficiency. The paper then presents a frequency-tuned saliency detection method that uses band-pass filters to retain a wide range of frequencies from the original image. The method is compared to five state-of-the-art saliency detection methods using frequency domain analysis, ground truth, and a salient object segmentation application. The proposed method outperforms the five algorithms in both ground-truth evaluation and segmentation tasks. The paper concludes that the proposed method provides better performance in terms of precision and recall, and is more suitable for salient object segmentation.This paper introduces a frequency-tuned salient region detection method that produces full-resolution saliency maps with well-defined boundaries of salient objects. The method retains more frequency content from the original image than existing techniques, leading to better performance in terms of precision and recall. It uses color and luminance features, is simple to implement, and is computationally efficient. The method is compared to five state-of-the-art salient region detection methods using frequency domain analysis, ground truth, and a salient object segmentation application. The proposed method outperforms the five algorithms in both ground-truth evaluation and segmentation tasks. The paper discusses the limitations of existing saliency maps, including low resolution, poorly defined object boundaries, and failure to uniformly map entire salient regions. It then presents a frequency-domain analysis of five state-of-the-art saliency detection methods, showing that they primarily use extremely low-frequency content. The proposed method introduces a frequency-tuned approach to estimate center-surround contrast using color and luminance features, offering three advantages: uniformly highlighted salient regions with well-defined boundaries, full resolution, and computational efficiency. The paper also discusses general approaches to determining saliency, including biological-based, purely computational, and hybrid methods. It highlights the limitations of existing methods, such as low resolution, ill-defined object boundaries, and failure to uniformly map entire salient regions. The paper then presents a frequency-domain analysis of five state-of-the-art saliency detection methods, showing that they primarily use extremely low-frequency content. The proposed method introduces a frequency-tuned approach to estimate center-surround contrast using color and luminance features, offering three advantages: uniformly highlighted salient regions with well-defined boundaries, full resolution, and computational efficiency. The paper then presents a frequency-tuned saliency detection method that uses band-pass filters to retain a wide range of frequencies from the original image. The method is compared to five state-of-the-art saliency detection methods using frequency domain analysis, ground truth, and a salient object segmentation application. The proposed method outperforms the five algorithms in both ground-truth evaluation and segmentation tasks. The paper concludes that the proposed method provides better performance in terms of precision and recall, and is more suitable for salient object segmentation.